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31 lines
979 B
31 lines
979 B
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2022, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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from tempfile import NamedTemporaryFile
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import networkx as nx
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import numpy as np
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from miplearn.h5 import H5File
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from miplearn.problems.stab import (
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MaxWeightStableSetData,
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build_stab_model,
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)
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from miplearn.solvers.abstract import AbstractModel
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def test_stab() -> None:
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data = MaxWeightStableSetData(
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graph=nx.cycle_graph(5),
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weights=np.array([1.0, 1.0, 1.0, 1.0, 1.0]),
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)
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for model in [
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build_stab_model(data),
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]:
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assert isinstance(model, AbstractModel)
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with NamedTemporaryFile() as tempfile:
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with H5File(tempfile.name) as h5:
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model.optimize()
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model.extract_after_mip(h5)
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assert h5.get_scalar("mip_obj_value") == -2.0
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